Guo Yanjie, Chen Xuefeng, Wang Shibin, Sun Ruobin, Zhao Zhibin
School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an 710049, China.
Sensors (Basel). 2017 May 18;17(5):1149. doi: 10.3390/s17051149.
The gearbox is one of the key components in wind turbines. Gearbox fault signals are usually nonstationary and highly contaminated with noise. The presence of amplitude-modulated and frequency-modulated (AM-FM) characteristics compound the difficulty of precise fault diagnosis of wind turbines, therefore, it is crucial to develop an effective fault diagnosis method for such equipment. This paper presents an improved diagnosis method for wind turbines via the combination of synchrosqueezing transform and local mean decomposition. Compared to the conventional time-frequency analysis techniques, the improved method which is performed in non-real-time can effectively reduce the noise pollution of the signals and preserve the signal characteristics, and hence is suitable for the analysis of nonstationary signals with high noise. This method is further validated by simulated signals and practical vibration data measured from a 1.5 MW wind turbine. The results confirm that the proposed method can simultaneously control the noise and increase the accuracy of time-frequency representation.
齿轮箱是风力涡轮机的关键部件之一。齿轮箱故障信号通常是非平稳的，并且被噪声严重污染。调幅调频（AM-FM）特性的存在增加了风力涡轮机精确故障诊断的难度，因此，开发一种针对此类设备的有效故障诊断方法至关重要。本文提出了一种通过同步挤压变换和局部均值分解相结合的风力涡轮机改进诊断方法。与传统的时频分析技术相比，该改进方法在非实时情况下执行，可以有效降低信号的噪声污染并保留信号特征，因此适用于对高噪声非平稳信号的分析。该方法通过模拟信号和从一台1.5兆瓦风力涡轮机测量的实际振动数据进一步验证。结果证实，所提出的方法可以同时控制噪声并提高时频表示的准确性。
